The Genom of Homo sapiens.pdf

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THE GENOME KNOWLEDGEBASE 241by the human authors and those recorded in the Gene Ontologydatabases. Several iterations of the draft modulemay be needed before the software validity checks, GKcurator, and peer reviewers all agree that a module isready for release.Recently, we have made increasing use of the “minijamboree,”a kickoff meeting in which the authors for arelated set of modules get together in the same locale tohash out the scope of the modules and work out the relationshipsamong them. This gives authors the chance tomeet with the GK staff and to receive individualized helpwith the GK data model and PowerPoint templates. Thefirst series of mini-jamborees have been piggybacked onrelevant Cold Spring Harbor Laboratory scientific meetings,thereby saving participants time and cost. However,given authors’ enthusiastic response to the mini-jamboreeformat, we are looking to increase the number andbroaden the location of these meetings.EVIDENCE TRACKINGIn principle, GK is a database of human biological processes.In practice, many of the processes that we understandcome from experiments that have been performedin bacteria, yeast, fruit fly, or other model organisms. Weuse a strict form of evidence tracking, which distinguishesbetween reactions that have been documented bydirect experimental evidence (e.g., on human tissue culturecells) and those that are inferred indirectly from workwith model organisms. In the former case, we create a GKReaction whose taxon is H. sapiens, and document it withcitations which demonstrate that the reaction occurs inhumans. In the latter case, we create two Reactions: a reactionoccurring in the model organism documented withthe appropriate direct literature citations, and an inferredhuman reaction that is indirectly supported by what isknown to occur in the model organism.The key reason for creating dual reactions in the modelorganism system and humans is to prevent their genomesfrom becoming “entangled” in the database. In a reactiondirectly confirmed in yeast, the Physical Entities involvedwill be yeast proteins accessioned in SwissProt or Swiss-Prot/TREMBL. Likewise, a reaction directly confirmedin human cells will refer to the relevant human proteins.When a reaction in yeast is used to indirectly support aputative reaction in human, we populate the human reactionwith the human orthologs of the yeast proteins whenthe correct orthologs are known, or create placeholderphysical entities for the human proteins when the correctorthologs are not known. In either case, we strictly avoidcreating human reactions that involve non-human proteins.This allows pathway builders and other reasoningalgorithms to traverse the reactions without risking nonsensicaljumps from one species to another.Our current approach is to fill out pathways completelyin humans, and to bring in reactions from model organismsonly when necessary to span a gap in knowledge.However, this data model leaves open the possibility offilling out pathways completely in several of the modelorganism species as well, allowing meaningful comparisonsamong them.GK SOFTWAREInternally, GK runs on top of a MySQL database(DuBois 2003), a popular open source relational databasemanagement system. The data model was tested withProtégé-2000 (Noy et al. 2001), an open source editor forframe-based knowledge systems, and implemented usinga Protégé to MySQL input/output adapter that we developedfor the project. The Web pages are driven by Perlscripts running under the open source Apache Web serverand using a Perl middleware layer that translates highlevelrequests into reactions and physical entities intolow-level SQL queries. We have also developed a Javainterface to the database and are in the process of developinga Java-based authoring and editing tool for GK.All software developed for GK is available for use underan open source license.DISCUSSIONGK is one of a small but growing number of pathwayorienteddatabases. The oldest and most mature entrant inthis field is EcoCyc (Karp et al. 2002), a database of E.coli metabolic pathways. Recently, the EcoCyc datamodel has been successfully used to represent pathwaysin Arabidopsis, yeast, and human. Like GK, the Cycdatabases use a frame-based representation of biologicalpathways that are centered around the concept of a reaction,although the technical details differ considerably,including Cyc’s use of the LISP programming language.However, the chief difference between the systems istheir scope: Cyc focuses on intermediate metabolism,whereas GK emphasizes higher-order interactions amongmacromolecules such as transcription and translation.The KEGG database of genomes contains considerableinformation on pathways, but, like Cyc, its focus is on intermediatemetabolism. A fundamental difference betweenthe GK and KEGG data models is that in KEGGpathways the catalyst is represented as an enzymatic activity,or EC number. However, the relationship betweenan enzymatic activity and a protein is not as simple as itmight seem: Proteins that are only distantly related mayhave the same EC number if they share a catalytic activity,and the same protein subunit may carry different enzymaticactivities depending on other subunits withwhich it is currently complexed. For this reason, it is notstraightforward to connect a KEGG reaction to a humangene product.The BioCarta project (http://www.biocarta.com) is orientedtoward high-level processes such as signal transduction,much as GK is. However, its primary product isa series of diagrams illustrating biological pathways, andthe project as a whole is a proprietary commercial enterprise.Last, there are BIND (Bader and Hogue 2000; Bader etal. 2001) and MINT (Zanzoni et al. 2002), two databasesof protein–protein interaction data, as well as such project-specificdatabases as the Alliance for Cell Signalling(AfCS; Li et al. 2002). These databases emphasize themanagement of large amounts of raw interaction datafrom high-throughput data sets, such as yeast two-hybrid
242 JOSHI-TOPE ET AL.and co-immunoprecipitation studies. Because they containthe cutting-edge, unverified information that GK explicitlyexcludes, they are entirely complementary to GK.In the near future, we hope to establish reciprocal linkswith BIND, AfCS, and other such resources.As noted earlier, GK has deliberately limited its scopein several areas in order to make the project manageable.One of the more controversial of these decisions was toomit tissue-specific expression information from the descriptionof proteins. In the GK world, all proteins are expressedin all cells of the human body. This decisionmakes it difficult to express, for example, differences inthe glycolytic pathways in liver and skeletal muscle, andultimately interferes with the ability of pathway traversalalgorithms to correctly detect impossible pathways. Ourreasoning is that not too far into the future there will beonline atlases of human tissue-specific protein expressionpatterns derived from oligonucleotide arrays and otherhigh-throughput technologies. This information willclose the gap in GK’s data set. In the meantime, we areusing a variety of workarounds to describe tissue-specificdifferences in GK pathways.How far has GK gone toward our ambitious goal of coveringall essential topics in human biology? It has beenroughly one year since GK went from development modeinto full production. During that time, we have releasedten modules spanning such topics as DNA replication,mRNA processing, RNA transcription, cell cycle checkpoints,and a fair chunk of intermediate metabolism.Roughly speaking, we complete a new module eachmonth, although the size and scope of each module variestremendously depending on its content.One crude measure is to count the number of SwissProtand SwissProt/TREMBL proteins that we have accessionedin GK and then to divide by the total number ofhuman proteins in SwissProt and SwissProt/TREMBL.By this measure, GK covers slightly more than 6% of theinformation space. However, this procedure is biaseddownward because it fails to account for the fact that aconsiderable number of the proteins in TREMBL comefrom gene predictions and have no known function. Abetter measure might involve estimating the number ofreactions described in medical textbooks or the review literature.We are currently debating how best to accomplishthis.Looking ahead, our biggest priority for the next year ofthe project is to develop more tools for mining and visualizingGK data. The database is only as good as the toolsit offers to bioinformaticists and other researchers for organizingand interpreting large-scale data sets. One proposedvisualization tool is a zoomable “starry sky” viewof the human physiome in which each protein occupies afixed position in the night sky and connecting lines definethe “constellation” pathways. By superimposing microarrayexpression data on this view, researchers could see ata glance which pathways are involved by a set of up- ordown-regulated genes.A second priority is to speed the authoring and curationprocess. As noted above, we have developed and arenow testing an authoring tool that allows authors to interactdirectly with the GK database. If this tool is successful,we will no longer have to rely on PowerPoint templates as the primary authoring tool. This will free upcurators from the task of transferring information fromPowerPoint into Protégé and allow us to recruit moreauthors.Finally, we need to bring more automation into the curatorialprocess. GK curators spend a considerableamount of time looking up literature references, matchinggene names to SwissProt accession numbers, and checkingthe descriptions of protein functions against Gene Ontologyterms. This process would be greatly aided by a setof software tools to generate a “pre-authored” documentthat would contain proposed Gene Ontology terms, proteinaccession numbers, and literature references for thetopic currently under review. Over the next year, we willexplore a number of approaches to such pre-authoringtools.ACKNOWLEDGMENTSThe development of Genome KnowledgeBase is supportedby grant R01 HG-002639 from the National HumanGenome Research Institute at the National Institutesof Health and a subcontract from the NIH-funded CellMigration Consortium.REFERENCESAshburner M., Ball C.A., Blake J.A., Botstein D., Butler H.,Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., EppigJ.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A.,Lewis S., Matese J.C., Richardson J.E., Ringwald M., RubinG.M., and Sherlock G. (The Gene Ontology Consortium).2000. Gene ontology: Tool for the unification of biology. Nat.Genet. 25: 25.Bader G.D.and Hogue C.W. 2000. BIND: A data specificationfor storing and describing biomolecular interactions, molecularcomplexes and pathways. Bioinformatics 16: 465.Bader G.D., Donaldson I., Wolting C., Ouellette B.F., PawsonT., and Hogue C.W. 2001. BIND: The Biomolecular InteractionNetwork Database. Nucleic Acids Res. 29: 242.Costanzo M.C., Hogan J.D., Cusick M.E., Davis B.P., FancherA.M., Hodges P.E., Kondu P., Lengieza C., Lew-Smith J.E.,Lingner C., Roberg-Perez K.J., Tillberg M., Brooks J.E., andGarrels J.I. 2000. The yeast proteome database (YPD) andCaenorhabditis elegans proteome database (WormPD):Comprehensive resources for the organization and comparisonof model organism protein information. Nucleic AcidsRes. 28: 73.DeRisi J.L., Iyer V.R., and Brown P.O. 1997. Exploring themetabolic and genetic control of gene expression on a genomicscale. Science 278: 680.DuBois P. 2003. MySQL cookbook. O’Reilly and Associates,Sebastopol, California.Fields S. and Song O. 1989. A novel genetic system to detectprotein-protein interactions. Nature 340: 245.Fleischmann R.D., Adams M.D., White O., Clayton R.A., KirknessE.F., Kerlavage A.R., Bult C.J., Tomb J.F., DoughertyB.A., Merrick J.M., McKenney K., Sutton G.G., FitzHughW., Fields C.A., Gocayne J.D., Scott J.D., Shirley R., Liu L.I.,Glodek A., Kelley J.M., Weidman J.F., Phillips C.A., SpriggsT., Hedblom E., and Cotton M.D., et al. 1995. Whole-genomerandom sequencing and assembly of Haemophilus influenzaeRd. Science 269: 496.Kanehisa M. and Goto S. 2000. KEGG: Kyoto encyclopedia ofgenes and genomes. Nucleic Acids Res. 28: 27.
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ForewordIn 2001, as we considered t
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The Finished Genome Sequence of Hom
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4 ROGERSFigure 2. Accumulation of h
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6 ROGERSFigure 4. Sequencing center
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8 ROGERSabcFigure 5. Ensembl view o
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10 ROGERS2000. Analysis of vertebra
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The Human Genome: Genes, Pseudogene
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VARIATION ON CHROMOSOME 7 15rived f
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VARIATION ON CHROMOSOME 7 17DNAs an
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VARIATION ON CHROMOSOME 7 19expecte
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VARIATION ON CHROMOSOME 7 21Drosoph
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Mutational Profiling in the Human G
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HUMAN MUTATIONAL PROFILING 25Anothe
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HUMAN MUTATIONAL PROFILING 27Figure
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HUMAN MUTATIONAL PROFILING 29Rieder
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32 SCHMUTZ ET AL.algorithm itself,
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34 SCHMUTZ ET AL.Figure 2. Genomic
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36 SCHMUTZ ET AL.compared. Some of
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Human Subtelomeric DNAH. RIETHMAN,
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HUMAN SUBTELOMERIC SEQUENCES 41The
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HUMAN SUBTELOMERIC SEQUENCES 43cate
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HUMAN SUBTELOMERIC SEQUENCES 45Figu
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HUMAN SUBTELOMERIC SEQUENCES 47pres
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50 COLLINSand expand the genomics r
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52 COLLINSFigure 2. A public-sector
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54 COLLINSdefine all the parts of t
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56 BENTLEYmon over many generations
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58 BENTLEYTable 1. Genetic Disease
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60 BENTLEY(Clark et al. 1998; Reich
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62 BENTLEYACKNOWLEDGMENTSThe author
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SNP Genotyping and Molecular Haplot
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GENETIC ANALYSIS OF DNA POOLS 67gen
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70 FAN ET AL.matrix is then mated t
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72 FAN ET AL.Figure 3. Views of gen
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74 FAN ET AL.including 32 duplicate
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76 FAN ET AL.Figure 7. Allele-speci
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78 FAN ET AL.microsphere-based assa
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80 BERTRANPETIT ET AL.function, may
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82 BERTRANPETIT ET AL.diversity in
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84 BERTRANPETIT ET AL.gree of block
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86 BERTRANPETIT ET AL.Figure 1. Dec
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88 BERTRANPETIT ET AL.1999. Populat
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90 WINDEMUTH ET AL.Expression data.
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92 WINDEMUTH ET AL.Table 1. A Summa
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94 WINDEMUTH ET AL.Table 2. Signifi
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96 WINDEMUTH ET AL.Table 3. Summary
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98 WINDEMUTH ET AL.Table 6. List of
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100 WINDEMUTH ET AL.Table 6. (Conti
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102 WINDEMUTH ET AL.Table 6. (Conti
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104 WINDEMUTH ET AL.much of a surpr
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106 WINDEMUTH ET AL.Given our resul
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Genetic Variation and the Control o
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GENETIC CONTROL OF TRANSCRIPTION 11
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GENETIC CONTROL OF TRANSCRIPTION 11
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Genome-wide Detection and Analysis
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RECENT SEGMENTAL DUPLICATIONS 117Fi
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RECENT SEGMENTAL DUPLICATIONS 119St
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RECENT SEGMENTAL DUPLICATIONS 121Co
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RECENT SEGMENTAL DUPLICATIONS 123Ho
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The Effects of Evolutionary Distanc
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EVOLUTIONARY DISTANCE AND GENE PRED
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EVOLUTIONARY DISTANCE AND GENE PRED
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Lineage-specific Expansion of KRAB
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EVOLUTION OF ZNF GENES 133Figure 2.
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EVOLUTION OF ZNF GENES 135Figure 4.
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EVOLUTION OF ZNF GENES 137get gene,
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EVOLUTION OF ZNF GENES 139Y., Goodw
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Sequence Organization and Functiona
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CENTROMERE ANNOTATION 143THE CENTRO
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CENTROMERE ANNOTATION 145Figure 4.
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CENTROMERE ANNOTATION 147CONCLUSION
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CENTROMERE ANNOTATION 149Schueler M
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152 PARKHILL AND THOMSONFigure 1. T
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154 PARKHILL AND THOMSONshow very h
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156 PARKHILL AND THOMSONGene Loss a
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158 PARKHILL AND THOMSONYersinia ad
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160 MCKAY ET AL.Choosing Candidate
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162 MCKAY ET AL.new comparative too
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164 MCKAY ET AL.rich. Based on a th
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166 MCKAY ET AL.Embryonic Muscle an
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168 MCKAY ET AL.native polyadenylat
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Building Comparative Maps Using 1.5
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HUMAN CHROMOSOME 1p IN THE DOG 1731
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HUMAN CHROMOSOME 1p IN THE DOG 175(
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HUMAN CHROMOSOME 1p IN THE DOG 177l
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180 GEORGES AND ANDERSSON5. There i
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182 GEORGES AND ANDERSSONplied to r
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184 GEORGES AND ANDERSSONbe common
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186 GEORGES AND ANDERSSONin humans
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Evolving Methods for the Assembly o
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Page 231 and 232: 224 ZHANGWe are waiting for experim
Page 234 and 235: Ontologies for Biologists: A Commun
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Page 273 and 274: 266 ROE ET AL.noncoding regions. On
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Evolution of Eukaryotic Gene Repert
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EVOLUTION OF EUKARYOTIC GENES AND I
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304 PENNACCHIO, BAROUKH, AND RUBINA
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306 PENNACCHIO, BAROUKH, AND RUBINh
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308 PENNACCHIO, BAROUKH, AND RUBINA
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High-Throughput Mouse Knockouts Pro
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314 FRIDDLE ET AL.screen to lines o
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Identification of Novel Functional
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100 bp ladder68G1168G1168H1168H6100
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FUNCTIONAL ELEMENTS IN HUMAN DNA 32
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High-resolution Human Genome Scanni
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HUMAN GENOME SCANNING 325false-posi
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HUMAN GENOME SCANNING 327affecting
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HUMAN GENOME SCANNING 329methods fo
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332 MALEK ET AL.Figure 1. The bacte
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334 MALEK ET AL.J., Vincent S., and
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336 HARDISON ET AL.reflect blocks o
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338 HARDISON ET AL.plain the region
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340 HARDISON ET AL.CALIBRATION OF T
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342 HARDISON ET AL.PositionRP2.3noE
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344 HARDISON ET AL.cific chromosoma
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346 WESTON ET AL.these differences
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348 WESTON ET AL.els controlled by
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350 WESTON ET AL.ures prominently i
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352 WESTON ET AL.nal and Bop, which
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354 WESTON ET AL.ablp 1466 bopbcrtB
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356 WESTON ET AL.like fold (Fig. 6)
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Implications of Genomics for Public
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GENETIC EPIDEMIOLOGY 361lytic epide
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GENETIC EPIDEMIOLOGY 363curate risk
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A Model System for Identifying Gene
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PTC TASTE GENETICS 367Figure 2. Hap
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PTC TASTE GENETICS 369Table 2. Hapl
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PTC TASTE GENETICS 371the emergence
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374 MCCALLION ET AL.Figure 1. Schem
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376 MCCALLION ET AL.lier (Carrasqui
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378 MCCALLION ET AL.Table 3. HSCR A
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380 MCCALLION ET AL.Figure 3. Trans
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Genetics of Schizophrenia and Bipol
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SCHIZOPHRENIA AND BIPOLAR AFFECTIVE
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The Genetics of Common Diseases: 10
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GENETICS OF COMMON DISEASES 397with
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GENETICS OF COMMON DISEASES 399SELE
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GENETICS OF COMMON DISEASES 401F.,
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404 CHEUNG ET AL.netic analysis. Ex
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406 CHEUNG ET AL.Figure 3. The expr
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Regulation of α-Synuclein Expressi
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α-SYNUCLEIN EXPRESSION AND PD 411T
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1. The levels of α-synuclein prote
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α-SYNUCLEIN EXPRESSION AND PD 415g
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418 BOTSTEINFigure 1. (A) Blectron
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420 BOTSTEINFigure 3. Cluster diagr
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422 BOTSTEINFigure 6. Kaplan-Meier
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424 BOTSTEINGarber M.E., Troyanskay
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426 ANTONARAKIS ET AL.1316192225283
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428 ANTONARAKIS ET AL.Figure 5. Sam
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430 ANTONARAKIS ET AL.POPULATION VA
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432 JORGENSEN ET AL.tive small mole
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434 JORGENSEN ET AL.FLAG-tagged pro
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436 JORGENSEN ET AL.visualization t
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438 JORGENSEN ET AL.AArp2/3 Complex
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Pathway40S440 JORGENSEN ET AL.ANutr
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442 JORGENSEN ET AL.Giaever G., Chu
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Genomic Disorders: Genome Architect
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GENOME ARCHITECTURE AND GENOMIC DIS
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Human Versus Chimpanzee Chromosome-
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HUMAN VS. CHIMP CHROMOSOME COMPARIS
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HUMAN VS. CHIMP CHROMOSOME COMPARIS
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Novel Transcriptional Units and Unc
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TRANSCRIPTIONAL UNITS AND GENE PAIR
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mtDNA Variation, Climatic Adaptatio
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mtDNA VARIATION 473Figure 3. Region
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ANALYSIS OF ADAPTIVE SELECTION FORR
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mtDNA VARIATION 477Figure 8. Temper
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Positive Selection in the Human Gen
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HUMAN-SPECIFIC EVOLUTIONARY CHANGES
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488 UNDERHILLorigin episodes, each
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490 UNDERHILLhaplogroups C through
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492 UNDERHILLO (Fig. 2e) that share
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The New Quantitative BiologyM.V. OL
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NEW QUANTITATIVE BIOLOGY 497alone.
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NEW QUANTITATIVE BIOLOGY 499There w
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NEW QUANTITATIVE BIOLOGY 501ceded,
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